AI Engineer

Location:

Need to be able to work EST timezone.

Remote | Full-time

Compensation: $175K - $250K

We are hiring on behalf of our client who is developing a cutting‑edge autonomous agent runtime focused on high‑frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real‑time market outcomes. The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade.

This is a high‑stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth.

• Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing.
• Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise.
• Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously.
• Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real‑time.

• Fleet Monitoring: Create higher‑order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents.
• Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design.
• Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies.

• Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine‑tuned, domain‑specific models.
• Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable.
• Domain‑Specific Training: Determine the efficacy of domain‑specific training over general‑purpose prompting and build the necessary pipelines for implementation.
• Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost‑efficiency at scale.

• Production ML Engineering: Proven experience training, deploying, and maintaining models that run in production and directly impact business outcomes.
• Reinforcement/Online Learning: Deep understanding of the practical challenges of learning from real‑world outcomes rather than static datasets.
• Closed‑Loop Systems: A track record of building systems where predictions lead to actions that generate outcomes, which then feed back into improved predictions.
• Software Engineering: Proficiency in Python is required, with additional comfort in Go or Type Script for production services. Experience building data pipelines and distributed systems is essential.

• Financial ML: Background in signal generation, alpha research, portfolio optimization, or execution.
• LLM Specialization: Experience with fine‑tuning and serving (PEFT/LoRA, vLLM, TGI) or custom inference pipelines.
• Multi‑Agent Systems: Experience designing environments where autonomous agents coordinate or learn from one another.
• Domain Knowledge: Background in on‑chain data, DeFi protocols, or sectors where agents make sequential decisions under uncertainty (e.g., robotics, game AI).

• Base Salary: $175,000 – $250,000 USD (dependent on location and experience).
• Equity: Approximately 1% initial stock grant, with significant valuation growth potential.
• Performan…